Molecular spectrometry is a well-known technique used to identify the characteristics of gas, liquid, and solid samples, wherein light is directed at a sample and the light reflected from, scattered by, and/or transmitted through the sample is then picked up by a photosensitive detector to be analyzed for changes in wavelength. These changes may provide information regarding the composition of the sample, its chemical bonds, and other features. As an example, FIG. 1 illustrates an example of a spectrum (sometimes referred to as an “exposure,” or simply a “reading”) obtained from a Raman spectrometer, wherein a laser is directed at a sample and the detector captures data regarding the light scattered from the sample. Here the spectrum data is presented as a plot of light intensity versus light wavelength, with wavelength being represented by pixel numbers from the detector (which is made of an array of detector elements/pixels, such as an array of CCD elements). The spectrum, and in particular the locations and amplitudes of the “peaks” therein, can be compared to libraries of previously-obtained reference spectra to obtain information about the sample, such as its identity and characteristics.
One issue with spectra captured by molecular spectrometers, and in particular Raman spectrometers, is that the spectra can be difficult to interpret owing to weak signals (i.e., the “peaks” present in FIG. 1), and/or high noise (the “background” shown between the peaks). In general, the higher a spectrum's signal-to-noise ratio, the easier it is to match it to reference spectra or otherwise process/interpret the spectrum. To understand the factors affecting spectral signals and noise, it is useful to further examine how a spectrometer collects a spectrum. Typically, the detector element/pixel array is exposed to the light from the sample for a period of time (the “exposure time”), and then the accumulated/integrated charge on each element or pixel is converted into a digital signal (which can then be presented in a form similar to FIG. 1, if desired). This signal at each pixel is typically proportional to the light intensity thereon, but the signal is subject to both systematic and random errors, giving rise to the background noise. One common systematic error is dark current, which is a steady accumulation of charge on an element/pixel even when there is no incident light on the pixel. Dark current is intrinsic to the operation of many photosensitive detectors, and thus is difficult or impossible to eliminate. Another common systematic error is electronic offset: each pixel's deviation from the sensitivity value that it was intended to have during manufacture. In other words, offset arises from variations in materials, manufacturing processes, and other factors which generate minor deviations from pixel to pixel, making certain pixels more sensitive than others. As for random errors, these can arise for many reasons, with a common example being cosmic rays: ambient charged particles which periodically strike a pixel and give rise to a transient, but often high, intensity reading at the pixel in question. These systematic and random errors are unique to each array pixel, and they combine to contribute to the noise (which is often referred to as the “background” or “background spectrum,” though it contains no true spectral data).
To reduce the effect of these errors, it is common to employ a “background subtraction” scheme. After the spectrometer captures an exposure (i.e., provides light to the detector to collect spectra from a sample), the detector is shuttered or otherwise isolated so that data can be collected from the detector without having any light incident thereon. Such data provide a background spectrum which should (ideally) reflect the systematic component of the background in a sample exposure, provided the sample exposure and the background exposure had the same exposure time. One can then compensate for the random component of the background by taking several background exposures (again ideally having the same exposure time) and combining them by averaging or similar methods, or otherwise processing them to remove aberrational pixel intensities. Pixel-by-pixel subtraction of the combined background exposures from the sample spectra can then assist in reducing the background.
In similar respects, it is also useful to collect several exposures from a sample, all having the same exposure time, and then combining the collected spectra (e.g., by averaging or simply summing them). The resulting combined spectrum diminishes the effect of random noise and exhibits an improved signal-to-noise ratio relative to the individual component spectra. The aforementioned background subtraction scheme can then be applied to the combined spectrum to further enhance the signal to noise ratio.
However, both background subtraction and spectral combination bear disadvantages. In both cases, the methods for combining the spectra—as by averaging the sample exposures and/or background exposures—take a significant amount of time owing to the need to collect multiple exposures. From the standpoint of the spectrometer operator, this represents time that the spectrometer is unavailable for use. With respect to backgrounds, a combined background could be generated once and stored for subsequent use, but it is unique to the sample exposure time selected by the spectrometer operator. Thus, to use a combined background which has been stored in advance, an operator is limited to use of sample exposure times equivalent to the background exposure times used for the component backgrounds of the combined background. For reasons discussed below, it is undesirable to be limited to a particular sample exposure time. Thus, if the operator wants to change the sample exposure time, a new background (or backgrounds) having the same exposure time must be collected.
Better signal to noise ratios can also be obtained by increasing the exposure time. However, there are practical limits on exposure time. Initially, if one increases sample exposure time, the background exposure time must be increased to match in order to subsequently achieve proper subtraction (as discussed above). The aggregate time to reach the final “noise-cleaned” spectrum is further increased if the aforementioned combination methods are used (e.g., averaging, summing, or similar methods), since the multiple sample exposures and/or background exposures used to make the combination—which, again, should have the same exposure time—lead to a geometric increase in overall time. Further, a specified exposure time may be too large for the sample being measured: the strength of the signals (the height of the peaks) can exceed the capacity of the spectrometer electronics, resulting in an overflow condition and an invalid spectrum. The spectrometer operator is then required to reduce the exposure time and repeat the measurement, leading to even further lost time.